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        <header><div class="featured-image-preview"><a href="/convolutional2/" target="_blank"><img loading="lazy" decoding="async"
         
         src="https://img-blog.csdnimg.cn/20200614194424177.png"
         alt="TensorFlow2.1入门学习笔记(13)——卷积神经网络LeNet, AlexNet, VGGNet示例"
         title="TensorFlow2.1入门学习笔记(13)——卷积神经网络LeNet, AlexNet, VGGNet示例" height="798" width="238"
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                </div><div style="padding-top: 20px;padding-left: 20px;padding-right: 20px;"><h2 class="summary-title" itemprop="name headline">
                    <a href="/convolutional2/" target="_blank">TensorFlow2.1入门学习笔记(13)——卷积神经网络LeNet, AlexNet, VGGNet示例</a>
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                            <a href="/categories/tf2.1%E5%AD%A6%E4%B9%A0%E7%AC%94%E8%AE%B0/"><span class="svg-icon icon-folder"></span>TF2.1学习笔记</a>
                    </span><span class="meta-item post-author"><a href="https://aimoon.top" title="Author" target="_blank" rel="noopener noreffer author" class="author"><span class="svg-icon icon-user"></span>Yasin</a></span><span class="meta-item post-publish"><span class="svg-icon icon-clock"></span><time class="timeago" datetime="2020-06-15">2020-06-15</time></span></div></div>
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        <div class="article-text"><div class="content summary-content">Cifar10数据集 提供 5万张 32*32 像素点的十分类彩色图片和标签，用于训练。 提供 1万张 32*32 像素点的十分类彩色图片和标签，用于测试。 导入cifar1</div></div>
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        <header><div class="featured-image-preview"><a href="/convolutional1/" target="_blank"><img loading="lazy" decoding="async"
         
         src="https://img-blog.csdnimg.cn/2020061223261222.png"
         alt="TensorFlow2.1入门学习笔记(12)——卷积神经网络"
         title="TensorFlow2.1入门学习笔记(12)——卷积神经网络" height="798" width="238"
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                </div><div style="padding-top: 20px;padding-left: 20px;padding-right: 20px;"><h2 class="summary-title" itemprop="name headline">
                    <a href="/convolutional1/" target="_blank">TensorFlow2.1入门学习笔记(12)——卷积神经网络</a>
                </h2><div class="post-meta summary-post-meta"><span class="meta-item post-category">
                            <a href="/categories/tf2.1%E5%AD%A6%E4%B9%A0%E7%AC%94%E8%AE%B0/"><span class="svg-icon icon-folder"></span>TF2.1学习笔记</a>
                    </span><span class="meta-item post-author"><a href="https://aimoon.top" title="Author" target="_blank" rel="noopener noreffer author" class="author"><span class="svg-icon icon-user"></span>Yasin</a></span><span class="meta-item post-publish"><span class="svg-icon icon-clock"></span><time class="timeago" datetime="2020-06-13">2020-06-13</time></span></div></div>
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        <div class="article-text"><div class="content summary-content">每个神经元与前后相邻层的每一个神经元都有连接关系，输入是特征，输出为预测的结果。随着隐藏层增多，网络规模的增大，待优化参数过多容易导致模型过</div></div>
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            <div class="post-tags-summary-under-footer-display" style="display: inline-block;"><a class="post-tag-summary" href="/tags/cnn/" style="">CNN</a><a class="post-tag-summary" href="/tags/%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C/" style="">神经网络</a></div>
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        <header><div class="featured-image-preview"><a href="/selfdatatrain/" target="_blank"><img loading="lazy" decoding="async"
         
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         alt="TensorFlow2.1入门学习笔记(11)——自制数据集，并记录训练模型"
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                </div><div style="padding-top: 20px;padding-left: 20px;padding-right: 20px;"><h2 class="summary-title" itemprop="name headline">
                    <a href="/selfdatatrain/" target="_blank">TensorFlow2.1入门学习笔记(11)——自制数据集，并记录训练模型</a>
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                            <a href="/categories/tf2.1%E5%AD%A6%E4%B9%A0%E7%AC%94%E8%AE%B0/"><span class="svg-icon icon-folder"></span>TF2.1学习笔记</a>
                    </span><span class="meta-item post-author"><a href="https://aimoon.top" title="Author" target="_blank" rel="noopener noreffer author" class="author"><span class="svg-icon icon-user"></span>Yasin</a></span><span class="meta-item post-publish"><span class="svg-icon icon-clock"></span><time class="timeago" datetime="2020-06-04">2020-06-04</time></span></div></div>
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        <div class="article-text"><div class="content summary-content">以MNIST的sequential模型为base-line，通过读取自己的数据，训练模型并存储模型，最后达到绘图实物的运用。 自制数据集，解决</div></div>
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            <div class="post-tags-summary-under-footer-display" style="display: inline-block;"><a class="post-tag-summary" href="/tags/datasets/" style="">datasets</a><a class="post-tag-summary" href="/tags/mnist/" style="">mnist</a></div>
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        <header><div class="featured-image-preview"><a href="/tf_keras_mnist/" target="_blank"><img loading="lazy" decoding="async"
         
         src="https://img-blog.csdnimg.cn/20200612232716115.png"
         alt="TensorFlow2.1入门学习笔记(10)——使用keras搭建神经网络(Mnist,Fashion)"
         title="TensorFlow2.1入门学习笔记(10)——使用keras搭建神经网络(Mnist,Fashion)" height="798" width="238"
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                </div><div style="padding-top: 20px;padding-left: 20px;padding-right: 20px;"><h2 class="summary-title" itemprop="name headline">
                    <a href="/tf_keras_mnist/" target="_blank">TensorFlow2.1入门学习笔记(10)——使用keras搭建神经网络(Mnist,Fashion)</a>
                </h2><div class="post-meta summary-post-meta"><span class="meta-item post-category">
                            <a href="/categories/tf2.1%E5%AD%A6%E4%B9%A0%E7%AC%94%E8%AE%B0/"><span class="svg-icon icon-folder"></span>TF2.1学习笔记</a>
                    </span><span class="meta-item post-author"><a href="https://aimoon.top" title="Author" target="_blank" rel="noopener noreffer author" class="author"><span class="svg-icon icon-user"></span>Yasin</a></span><span class="meta-item post-publish"><span class="svg-icon icon-clock"></span><time class="timeago" datetime="2020-05-31">2020-05-31</time></span></div></div>
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        <div class="article-text"><div class="content summary-content">前面已经使用TensorFlow2的原生代码搭建神经网络，接下来将使用keras搭建神经网络，并改写鸢尾花分类问题的代码，将原本100多行的</div></div>
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        <header><div class="featured-image-preview"><a href="/thermodynamic1/" target="_blank"><img loading="lazy" decoding="async"
         
         src="https://img-blog.csdnimg.cn/20200612232746346.png"
         alt="热力学第一定律 等值过程 绝热过程"
         title="热力学第一定律 等值过程 绝热过程" height="798" width="238"
    /></a>
                </div><div style="padding-top: 20px;padding-left: 20px;padding-right: 20px;"><h2 class="summary-title" itemprop="name headline">
                    <a href="/thermodynamic1/" target="_blank">热力学第一定律 等值过程 绝热过程</a>
                </h2><div class="post-meta summary-post-meta"><span class="meta-item post-category">
                            <a href="/categories/%E7%83%AD%E5%8A%9B%E5%AD%A6/"><span class="svg-icon icon-folder"></span>热力学</a>
                    </span><span class="meta-item post-author"><a href="https://aimoon.top" title="Author" target="_blank" rel="noopener noreffer author" class="author"><span class="svg-icon icon-user"></span>Yasin</a></span><span class="meta-item post-publish"><span class="svg-icon icon-clock"></span><time class="timeago" datetime="2020-05-30">2020-05-30</time></span></div></div>
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        <div class="article-text"><div class="content summary-content">热力学第一定律内能，功和热量 实际气体内能：所有热分子热运动的动能和分子势能的总和 内能是状态量: $E=E(T,V)$ 理想气体内能: $E={\frac{M}{M_{mol}}{\frac{i}{2}}RT}$ 是状态参量T的单值函数 系统内</div></div>
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            <div class="post-tags-summary-under-footer-display" style="display: inline-block;"><a class="post-tag-summary" href="/tags/%E7%AD%89%E5%AE%B9/" style="">等容</a><a class="post-tag-summary" href="/tags/%E7%AD%89%E6%B8%A9/" style="">等温</a><a class="post-tag-summary" href="/tags/%E7%AD%89%E5%8E%8B/" style="">等压</a><a class="post-tag-summary" href="/tags/%E4%BD%93%E7%A7%AF%E5%8A%9F/" style="">体积功</a></div>
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        <header><div class="featured-image-preview"><a href="/optimizer/" target="_blank"><img loading="lazy" decoding="async"
         
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         alt="TensorFlow2.1入门学习笔记(9)——神经网络参数优化器(优化器性能比较)"
         title="TensorFlow2.1入门学习笔记(9)——神经网络参数优化器(优化器性能比较)" height="798" width="238"
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                </div><div style="padding-top: 20px;padding-left: 20px;padding-right: 20px;"><h2 class="summary-title" itemprop="name headline">
                    <a href="/optimizer/" target="_blank">TensorFlow2.1入门学习笔记(9)——神经网络参数优化器(优化器性能比较)</a>
                </h2><div class="post-meta summary-post-meta"><span class="meta-item post-category">
                            <a href="/categories/tf2.1%E5%AD%A6%E4%B9%A0%E7%AC%94%E8%AE%B0/"><span class="svg-icon icon-folder"></span>TF2.1学习笔记</a>
                    </span><span class="meta-item post-author"><a href="https://aimoon.top" title="Author" target="_blank" rel="noopener noreffer author" class="author"><span class="svg-icon icon-user"></span>Yasin</a></span><span class="meta-item post-publish"><span class="svg-icon icon-clock"></span><time class="timeago" datetime="2020-05-21">2020-05-21</time></span></div></div>
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        <div class="article-text"><div class="content summary-content">神经网络是基于链接的人工智能，当网络结构固定后，不同参数的选取对模型的表达力影响很大，优化器就是引导更新模型参数的工具 常用符号 待优化参数w 损</div></div>
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        <header><div class="featured-image-preview"><a href="/regularization/" target="_blank"><img loading="lazy" decoding="async"
         
         src="https://img-blog.csdnimg.cn/20200612232746282.png"
         alt="TensorFlow2.1入门学习笔记(8)——欠拟合与过拟合(正则化)"
         title="TensorFlow2.1入门学习笔记(8)——欠拟合与过拟合(正则化)" height="798" width="238"
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                </div><div style="padding-top: 20px;padding-left: 20px;padding-right: 20px;"><h2 class="summary-title" itemprop="name headline">
                    <a href="/regularization/" target="_blank">TensorFlow2.1入门学习笔记(8)——欠拟合与过拟合(正则化)</a>
                </h2><div class="post-meta summary-post-meta"><span class="meta-item post-category">
                            <a href="/categories/tf2.1%E5%AD%A6%E4%B9%A0%E7%AC%94%E8%AE%B0/"><span class="svg-icon icon-folder"></span>TF2.1学习笔记</a>
                    </span><span class="meta-item post-author"><a href="https://aimoon.top" title="Author" target="_blank" rel="noopener noreffer author" class="author"><span class="svg-icon icon-user"></span>Yasin</a></span><span class="meta-item post-publish"><span class="svg-icon icon-clock"></span><time class="timeago" datetime="2020-05-21">2020-05-21</time></span></div></div>
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        <div class="article-text"><div class="content summary-content">欠拟合与过拟合 欠拟合：模型不能有效拟合数据集对现有数据集学习的不够彻底 过拟合：模型对训练集拟合的太好，而缺失了泛化力 欠拟合的解决方法： 增加输</div></div>
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                    <a href="/loss/" target="_blank">TensorFlow2.1入门学习笔记(7)——损失函数</a>
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                            <a href="/categories/tf2.1%E5%AD%A6%E4%B9%A0%E7%AC%94%E8%AE%B0/"><span class="svg-icon icon-folder"></span>TF2.1学习笔记</a>
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        <div class="article-text"><div class="content summary-content">损失函数（loss）：预测值（y）与已知答案（y_）的差距 神经网络的优化目标： loss最小: $\Rightarrow\left\{\begin{array}{lr}{mse(Mean Aquared Error)}\\{自定义}\\{ce(Cr</div></div>
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                    <a href="/activation/" target="_blank">TensorFlow2.1入门学习笔记(6)——激活函数</a>
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                            <a href="/categories/tf2.1%E5%AD%A6%E4%B9%A0%E7%AC%94%E8%AE%B0/"><span class="svg-icon icon-folder"></span>TF2.1学习笔记</a>
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        <div class="article-text"><div class="content summary-content">常见函数tf.where(条件语句，真返回A，假返回B)1 2 3 4 5 6 import tensorflow as tf a = tf.constant([1, 2, 3, 1, 1]) b = tf.constant([0, 1, 3, 4, 5]) # 若a&gt;b，返回a对应位置的元</div></div>
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                    <a href="/firstnn_iris/" target="_blank">TensorFlow2.1入门学习笔记(5)——构建第一个神经网络，鸢尾花分类问题（附源码）</a>
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        <div class="article-text"><div class="content summary-content">根据前面的基础知识，可以开始第一个神经网络的搭建，主要学习的资料西安科技大学：神经网络与深度学习——TensorFlow2.0实战，北京大学</div></div>
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                    <a href="/calculate/" target="_blank">TensorFlow2.1入门学习笔记(4)——神经网络计算</a>
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        <div class="article-text"><div class="content summary-content">前面已经学习了有关TensorFlow的一些常用库，以及相关数据的处理方式，下面就是有关神经网络计算的学习笔记。主要学习的资料西安科技大学：</div></div>
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        <div class="article-text"><div class="content summary-content"><p>在正式学习tensorflow2.0之前需要有一定的python基础，对numpy，matplotlib等库有基本的了解，笔者还是AI小白，通过写博客来记录自己的学习过程，同时对所学的东西进行总结。主要学习的资料西安科技大学：<a href="https://www.icourse163.org/learn/XUST-1206363802#/learn/announce" target="_blank" rel="noopener noreffer">神经网络与深度学习——TensorFlow2.0实战</a>，北京大学：<a href="https://www.icourse163.org/learn/PKU-1002536002#/learn/announce" target="_blank" rel="noopener noreffer">人工智能实践Tensorflow笔记</a>博客从tf常用的库开始，需要学习python基础的朋友推荐<a href="https://www.runoob.com/python3/python3-tutorial.html" target="_blank" rel="noopener noreffer">菜鸟教程</a></p></div></div>
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        <div class="article-text"><div class="content summary-content">在正式学习tensorflow2.0之前需要有一定的python基础，对numpy，matplotlib等库有基本的了解，笔者还是AI小白，</div></div>
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        <div class="article-text"><div class="content summary-content">在正式学习tensorflow2.0之前需要有一定的python基础，对numpy，matplotlib等库有基本的了解，笔者还是AI小白，</div></div>
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